documet-slar

说明：  用python进行forensic，经典书籍，黑客必备！！！！！！！！(Master the art of digital forensics and analysis with Python)

说明：  传智书城网络书城源代码，适合新手下载学习。用户登录，注册，商品的添加删除以及后台的管理程序(Chuanzhi Bookstore Online Bookstore Source Code, suitable for beginners to download and learn. User login, registration, merchandise addition and deletion, and background management procedures)

说明：  基于simulink搭建的移相全桥仿真模型，闭环控制算法(P S F B,P I D FEED BACK CONTROL)

win32 二进制，显示hello world(Win32 binary, showing Hello World)

说明：  用N来表示训练用例（样本）的个数，M表示特征数目。 输入特征数目m，用于确定决策树上一个节点的决策结果；其中m应远小于M。 从N个训练用例（样本）中以有放回抽样的方式，取样N次，形成一个训练集（即bootstrap取样），并用未抽到的用例（样本）作预测，评估其误差。 对于每一个节点，随机选择m个特征，决策树上每个节点的决定都是基于这些特征确定的。根据这m个特征，计算其最佳的分裂方式。 每棵树都会完整成长而不会剪枝，这有可能在建完一棵正常树状分类器后会被采用）。(N is used to represent the number of training cases (samples), and M is used to represent the number of features. The number of input features m is used to determine the decision result of a node in the decision tree, where m should be far less than m. From N training cases (samples), n times are sampled in the way of put back sampling to form a training set (i.e. bootstrap sampling), and the unselected cases (samples) are used to predict and evaluate the error. For each node, m features are randomly selected, and the decision of each node in the decision tree is determined based on these features. According to these m characteristics, the best splitting mode is calculated. Each tree will grow completely without pruning, which may be adopted after building a normal tree classifier).)

说明：  有关于D2D通信中，信道仿真的编程是基于python语言的。(Programming for channel emulation in D2D communication)

MatlabGUI转EXE(合订)文档(MATLAB GUI to EXE Document)

不是说了吗k邻域算法，ieee9的k邻域算法，拿好不用谢(Did I say K neighborhood algorithm?)

uart EDMA数据收发测试例程，仅供学习使用(uart EDMA data send and rcv)

stc12c2052单片机有关ad转换的一些代码-wu